Class structure visualization with semi-supervised growing self-organizing maps

Authors:
Arthur Hsu;Saman K. Halgamuge
Affiliations:
Department of Mechanical Engineering, University of Melbourne, Victoria 3010, Australia;Department of Mechanical Engineering, University of Melbourne, Victoria 3010, Australia
Venue:
Neurocomputing
Year:
2008

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Abstract

We present a semi-supervised learning method for the growing self-organising maps (GSOM) that allows fast visualisation of data class structure on the 2D feature map. Instead of discarding data with missing values, the network can be trained from data with up to 60% of their class labels and 25% of attribute values missing, while able to make class prediction with over 90% accuracy for the benchmark datasets used. The proposed algorithm is compared to three variants of semi-supervised K-means learning on four real-world benchmark datasets and showed comparable performance and better generalisation.